Come usare
- Copia il comando e controlla il pacchetto prima di installarlo nel tuo ambiente agente.
- Controlla strumenti, credenziali e ambienti richiesti nel documento originale.
- Descrivi input e risultato desiderato; confronta l’output con le fonti.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill extract-empirical-regularityPrima di iniziare
- L’installazione non include abbonamenti, quote API o accesso ai file.
- Stelle e installazioni indicano attività, non un test indipendente di qualità o sicurezza.
Documento Skill originale
Contenuto dal repository con titoli, esempi, codice, tabelle, link e immagini preservati.
extract-empirical-regularity
Purpose
Extract a repeatable empirical pattern from records while preserving conditions, exceptions, and uncertainty.
Input contract
yaml
required: [observations, variable_schema, condition_schema]
optional: [replication_records, measurement_uncertainty]
constraints: [regularity claims require multiple comparable observations or an explicit single-case limitation]Procedure
- Normalize observations, units, and conditions.
- Identify repeated associations, trends, or invariants.
- Test exceptions, alternative explanations, and measurement artifacts.
- State the regularity with scope and uncertainty boundaries.
If the regularity is stable enough to express through measurable factors and outcomes, consider identify-variables as the next tactic.
Output contract
yaml
produces: [regularity_statement, supporting_records, exception_set, scope_conditions]
delta_fields: [findings, evidence_updates, uncertainties, open_questions]Quality gates
- Conditions and exceptions accompany every regularity.
- Correlation is not labeled mechanism without supporting evidence.
Failure and counterexamples
Do not generalize a pattern across changed populations or protocols without a comparability check.
Provenance map
resolved: extract-empirical-regularity
